Precoder design for multi-antenna transmission in MU-MIMO with QoS requirements

Abstract. A multiple-input multiple-output (MIMO) interference broadcast channel (IBC) channel is considered. There are several base stations (BSs) transmitting useful information to their own users and unwanted interference to its neighboring BS users. Our main interest is to maximize the system throughput by designing transmit precoders with weighted sum rate maximization (WSRM) objective for a multi-user (MU)-MIMO transmission. In addition, we include the quality of service (QoS) requirement in terms of guaranteed minimum rate for the users in the system. Unfortunately, the problem considered is nonconvex and known to be non-deterministic polynomial (NP) hard. Therefore, to determine the transmit precoders, we first propose a centralized precoder design by considering two closely related approaches, namely, direct signal-to-interference-plus-noise-ratio (SINR) relaxation via sequential parametric convex approximation (SPCA), and mean squared error (MSE) reformulation. In both approaches, we adopt successive convex approximation (SCA) technique to solve the nonconvex optimization problem by solving a sequence of convex subproblems. Due to the huge signaling requirements in the centralized design, we propose two different distributed precoder designs, wherein each BS determines only the relevant set of transmit precoders by exchanging minimal information among the coordinating BSs. Initially, we consider designing precoders in a decentralized manner by using alternating directions method of multipliers (ADMM), wherein each BS relaxes inter-cell interference as an optimization variable by including it in the objective. Then, we also propose a distributed precoder design by solving the Karush-Kuhn-Tucker (KKT) expressions corresponding to the centralized problems. Numerical simulations are provided to compare different system configurations with QoS constraints for both centralized and distributed algorithms

Resource Management in Cloud-based Radio Access Networks: a Distributed Optimization Perspective

University of Minnesota Ph.D. dissertation. 2015. Major: Electrical Engineering. Advisor: Zhi-Quan Luo. 1 computer file (PDF); ix, 136 pages.In this dissertation, we consider the base station (BS) and the resource management problems for the cloud-based radio access network (C-RAN). The main difference of the envisioned future 5G network architecture is the adoption of multi-tier BSs to extend the coverage of the existing cellular BSs. Each of the BS is connected to the multi-hop backhaul network with limited bandwidth. For provisioning the network, the cloud centers have been proposed to serve as the control centers. These differences give rise to many practical challenges. The main focus of this dissertation is the distributed strategy across the cloud centers. First, we show that by jointly optimizing the transceivers and determining the active set of BSs, high system resource utilization can be achieved with only a small number of BSs. In particular, we provide efficient distributed algorithms for such joint optimization problem, under the following two common design criteria: i) minimization of the total power consumption at the BSs, and ii) maximization of the system spectrum efficiency. In both cases, we introduce a nonsmooth regularizer to facilitate the activation of the most appropriate BSs, and the algorithms are, respectively, developed with Alternating Direction Method of Multipliers (ADMM) and weighted minimum mean square error (WMMSE) algorithm. In the second part, we further explicitly consider the backhaul limitation issues. We propose an efficient algorithm for joint resource allocation across the wireless links and the flow control over the entire network. The algorithm, which maximizes the utility function of the rates among all the transmitted commodities, is based on a decomposition approach leverages both the ADMM and the WMMSE algorithms. This algorithm is shown to be easily parallelizable within cloud centers and converges globally to a stationary solution. Lastly, since ADMM has been popular for solving large-scale distributed convex optimization, we further consider the issues of the network synchronization across the cloud centers. We propose an ADMM-type implementation that can handle a specific form of asynchronism based on the so-called BSUM-M algorithm, a new variant of ADMM. We show that the proposed algorithm converges to the global optimal solution

D13.2 Techniques and performance analysis on energy- and bandwidth-efficient communications and networking

Deliverable D13.2 del projecte europeu NEWCOM#The report presents the status of the research work of the various Joint Research Activities (JRA) in WP1.3 and the results that were developed up to the second year of the project. For each activity there is a description, an illustration of the adherence to and relevance with the identified fundamental open issues, a short presentation of the main results, and a roadmap for the future joint research. In the Annex, for each JRA, the main technical details on specific scientific activities are described in detail.Peer ReviewedPostprint (published version

Federated Learning and Meta Learning:Approaches, Applications, and Directions

Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data is collected at a central server for training. However, this approach poses a challenge in terms of preserving the data privacy of devices. To address this issue, federated learning (FL) has emerged as an effective solution that allows edge devices to collaboratively train ML models without compromising data privacy. In FL, local datasets are not shared, and the focus is on learning a global model for a specific task involving all devices. However, FL has limitations when it comes to adapting the model to devices with different data distributions. In such cases, meta learning is considered, as it enables the adaptation of learning models to different data distributions using only a few data samples. In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks. We also analyze the relationships among these learning algorithms and examine their advantages and disadvantages in real-world applications.</p